The construction industry utilizes many materials that go through different manufacturing and transportation processes. Selecting and using environmentally friendly or green building materials is one way to reduce the inimical environmental effects associated with the construction industry. However, environmental preference cannot be used as the only criterion in selecting a building material. As cost is an important factor, environmental performance needs to be balanced against the economic performance. The decisions on material selection, therefore, need improvement. This study analyzed and compared the environmental and economic performance of asphalt shingle and clay tile roofing materials using the building for environmental and economic sustainability (BEES) model. The results were then used to develop a combined performance score using the technique for order preference by similarity to ideal solution (TOPSIS) to assist decisions pertaining to the selection of the best material based on the performance score. To present another decision criteria to compare and select a roof material, the study utilized the contingent valuation method (CVM) to estimate the environmental cost associated with these alternative materials. The simulation results showed that clay tile had a better environmental performance score of 0.3525 points ðptsÞ=unit compared to the score of 0.4135 pts=unit for asphalt shingles; however, asphalt shingles had a better overall performance score of 0.5985 pts against 0.4153 pts for the clay tile. Merging environmental and economic considerations to a common score as presented in this study will help improve and contribute to the quality of decision making in that it provides a substantial basis to select the most suitable, cost-effective roofing material.

The Program Evaluation and Review Technique (PERT) model uses parameters such as the specified project completion time, mean, and variance to estimate the probability of project completion time. However, this model uses a weighted average and unweighted value in the variance, which is based on six sigma of the mean. Despite many proposed modifications to improve the traditional PERT model, the hidden error in the calculation of the variance and mean of the PERT approach has not been adequately addressed. This error leads to underestimation of the schedule risk. Considering the impact of variance and mean on the probability of project completion times, this study contributes to the improvement of the accuracy of schedule risk estimation by proposing a modified variance and mean of the original PERT model. The original PERT model was first used to estimate the project completion time. However, using the proposed modified model to estimate the completion time, a 95% confidence interval assumption and the corresponding distribution within ±2 standard deviation of the mean and standard or Z values were employed to model the new mean and variance equations. To prove the validity of the proposed modified variance and mean assumptions, we performed a schedule risk analysis through simulation using Oracle Crystal Ball for comparison. The results showed that the proposed PERT model had a better mean error rate of 2.46% as compared to 3.31% of the original PERT model.

To combat the rutting effect and other distresses in asphalt concrete pavement, certain modifiers and additives have been developed to modify the asphalt mixture to improve its performance. Although few additives exist, nanomaterials have recently attracted significant attention from the pavement industry. Several experimental studies have shown that the use of nanomaterials to modify asphalt binder results in an improved oxidative aging property, increased resistance to the rutting effect, and improves the rheological properties of the asphalt mixture. However, despite the numerous benefits of using nanomaterials in asphalt binders and materials, there are various uncertainties regarding the environmental impacts of nano-modified asphalt mixtures (NMAM). Therefore, this study assessed a Nano-Silica-Modified Asphalt Mixtures in terms of materials production emissions through the Life Cycle Assessment methodology (LCA), and the results were compared to a conventional asphalt mixture to understand the impact contribution of nano-silica in asphalt mixtures. To be able to compare the relative significance of each impact category, the normalized score for each impact category was calculated using the impact scores and the normalization factors. The results showed that NMAM had a global warming potential of 7.44563 × 103 kg CO2-Eq per functional unit (FU) compared to 7.41900 × 103 kg CO2-Eq per functional unit of the conventional asphalt mixture. The application of LCA to NMAM has the potential to guide decision-makers on the selection of pavement modification additives to realize the benefits of using nanomaterials in pavements while avoiding potential environmental risks.

The human knowledge of a specific domain is dissipated either in books and journals or exists in the minds of few human experts. Expert system technology, which is of late becoming an important tool, uses the power of the human brain to store knowledge electronically so that information regarding decision-making can easily be accessed at anywhere and at any time. More often than not most decisions in the industry are based on just subjective decisions. But since each project is different from the other, there is the need to integrate heuristic approach in other to arrive at better decisions. Therefore the system, ESCONPROCS (Expert system for construction procurement selection) which is an expert system tool was developed based on extensive literature review of the available procurement systems as well as clients' priorities and other external factors that influence the selection of an appropriate procurement system. An expert survey was conducted and their responses were used to provide a recommendation for the rules. The system, ESCONPROCS is developed to assist decision-maker (client) reaches a more informed decision on procurement system selection and contracting.

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